Context-dependent reduction in corticomuscular coupling for balance control in chronic stroke survivors.

Balance control is an important indicator of mobility and independence in activities of daily living. How the functional coupling between the cortex and the muscle for balance control is affected following stroke remains to be known. We investigated the changes in coupling between the cortex and leg muscles during a challenging balance task over multiple frequency bands in chronic stroke survivors. Fourteen participants with stroke and ten healthy controls performed a challenging balance task. They stood on a computerized support surface that was either fixed (low difficulty condition) or sway-referenced with varying gain (medium and high difficulty conditions). We computed corticomuscular coherence between electrodes placed over the sensorimotor area (electroencephalography) and leg muscles (electromyography) and assessed balance performance using clinical and laboratory-based tests. We found significantly lower delta frequency band coherence in stroke participants when compared with healthy controls under medium difficulty condition, but not during low and high difficulty conditions. These differences were found for most of the distal but not for proximal leg muscle groups. No differences were found at other frequency bands. Participants with stroke showed poor balance clinical scores when compared with healthy controls, but no differences were found for laboratory-based tests. The observation of effects at distal but not at proximal muscle groups suggests differences in the (re)organization of the descending connections across two muscle groups for balance control. We argue that the observed group difference in delta band coherence indicates balance context-dependent alteration in mechanisms for the detection of somatosensory modulation resulting from sway-referencing of the support surface for balance maintenance following stroke.

Brain-machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study.

BACKGROUND: This research focused on the development of a motor imagery (MI) based brain-machine interface (BMI) using deep learning algorithms to control a lower-limb robotic exoskeleton. The study aimed to overcome the limitations of traditional BMI approaches by leveraging the advantages of deep learning, such as automated feature extraction and transfer learning. The experimental protocol to evaluate the BMI was designed as asynchronous, allowing subjects to perform mental tasks at their own will. METHODS: A total of five healthy able-bodied subjects were enrolled in this study to participate in a series of experimental sessions. The brain signals from two of these sessions were used to develop a generic deep learning model through transfer learning. Subsequently, this model was fine-tuned during the remaining sessions and subjected to evaluation. Three distinct deep learning approaches were compared: one that did not undergo fine-tuning, another that fine-tuned all layers of the model, and a third one that fine-tuned only the last three layers. The evaluation phase involved the exclusive closed-loop control of the exoskeleton device by the participants' neural activity using the second deep learning approach for the decoding. RESULTS: The three deep learning approaches were assessed in comparison to an approach based on spatial features that was trained for each subject and experimental session, demonstrating their superior performance. Interestingly, the deep learning approach without fine-tuning achieved comparable performance to the features-based approach, indicating that a generic model trained on data from different individuals and previous sessions can yield similar efficacy. Among the three deep learning approaches compared, fine-tuning all layer weights demonstrated the highest performance. CONCLUSION: This research represents an initial stride toward future calibration-free methods. Despite the efforts to diminish calibration time by leveraging data from other subjects, complete elimination proved unattainable. The study's discoveries hold notable significance for advancing calibration-free approaches, offering the promise of minimizing the need for training trials. Furthermore, the experimental evaluation protocol employed in this study aimed to replicate real-life scenarios, granting participants a higher degree of autonomy in decision-making regarding actions such as walking or stopping gait.

Electrospinography for non-invasively recording spinal sensorimotor networks in humans.

Objective. Currently, few non-invasive measures exist for directly measuring spinal sensorimotor networks. Electrospinography (ESG) is one non-invasive method but is primarily used to measure evoked responses or for monitoring the spinal cord during surgery. Our objectives were to evaluate the feasibility of ESG to measure spinal sensorimotor networks by determining spatiotemporal and functional connectivity changes during single-joint movements at the spinal and cortical levels.Approach. We synchronously recorded electroencephalography (EEG), electromyography, and ESG in ten neurologically intact adults while performing one of three lower-limb tasks (no movement, plantar-flexion and knee flexion) in the prone position. A multi-pronged approach was applied for removing artifacts usingH∞filtering, artifact subspace reconstruction and independent component (IC) analysis. Next, data were segmented by task and ICs of EEG were clustered across participants. Within-participant analysis of ICs and ESG data was conducted, and ESG was characterized in the time and frequency domains. Generalized partial directed coherence analysis was performed within ICs and between ICs and ESG data by participant and task.Results.K-means clustering resulted in five clusters of ICs at Brodmann areas (BAs) 9, BA 8, BA 39, BA 4, and BA 22. Areas associated with motor planning, working memory, visual processing, movement, and attention, respectively. Time-frequency analysis of ESG data found localized changes during movement execution when compared to no movement. Lastly, we found bi-directional changes in functional connectivity (p < 0.05, adjusted for multiple comparisons) within IC's and between IC's and ESG sensors during movement when compared to the no movement condition.Significance. To our knowledge this is the first report using high density ESG for characterizing single joint lower limb movements. Our findings provide support that ESG contains information about efferent and afferent signaling in neurologically intact adults and suggests that we can utilize ESG to directly study the spinal cord.

An empirical comparison of deep learning explainability approaches for EEG using simulated ground truth.

Recent advancements in machine learning and deep learning (DL) based neural decoders have significantly improved decoding capabilities using scalp electroencephalography (EEG). However, the interpretability of DL models remains an under-explored area. In this study, we compared multiple model explanation methods to identify the most suitable method for EEG and understand when some of these approaches might fail. A simulation framework was developed to evaluate the robustness and sensitivity of twelve back-propagation-based visualization methods by comparing to ground truth features. Multiple methods tested here showed reliability issues after randomizing either model weights or labels: e.g., the saliency approach, which is the most used visualization technique in EEG, was not class or model-specific. We found that DeepLift was consistently accurate as well as robust to detect the three key attributes tested here (temporal, spatial, and spectral precision). Overall, this study provides a review of model explanation methods for DL-based neural decoders and recommendations to understand when some of these methods fail and what they can capture in EEG.

Task-dependent Alteration in Delta Band Corticomuscular Coherence during Standing in Chronic Stroke Survivors.

Balance control is an important indicator of mobility and independence in activities of daily living. How the changes in functional integrity of corticospinal tract due to stroke affects the maintenance of upright stance remains to be known. We investigated the changes in functional coupling between the cortex and lower limb muscles during a challenging balance task over multiple frequency bands in chronic stroke survivors. Eleven stroke patients and nine healthy controls performed a challenging balance task. They stood on a computerized platform with/without somatosensory input distortion created by sway-referencing the support surface, thereby varying the difficulty levels of the task. We computed corticomuscular coherence between Cz (electroencephalography) and leg muscles and assessed balance performance using Berg Balance scale (BBS), Timed-up and go (TUG) and center of pressure (COP) measures. We found lower delta frequency band coherence in stroke patients when compared with healthy controls under medium difficulty condition for distal but not proximal leg muscles. For both groups, we found similar coherence at other frequency bands. On BBS and TUG, stroke patients showed poor balance. However, similar group differences were not consistently observed across COP measures. The presence of distal versus proximal effect suggests differences in the (re)organization of the corticospinal connections across the two muscles groups for balance control. We argue that the observed group difference in the delta coherence might be due to altered mechanisms for the detection of somatosensory modulation resulting from sway-referencing of the support platform for balance control.

Design and Validation of a Low-Cost Mobile EEG-Based Brain-Computer Interface.

Objective: We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain-computer (BCI) interface and internet-of-things (IoT) applications. Approach: The EEG-based BCI headset was designed from commercial off-the-shelf (COTS) components using a multi-pronged approach that balanced interoperability, cost, portability, usability, form factor, reliability, and closed-loop operation. Main Results: The adjustable headset was designed to accommodate 90% of the population. A patent-pending self-positioning dry electrode bracket allowed for vertical self-positioning while parting the user's hair to ensure contact of the electrode with the scalp. In the current prototype, five EEG electrodes were incorporated in the electrode bracket spanning the sensorimotor cortices bilaterally, and three skin sensors were included to measure eye movement and blinks. An inertial measurement unit (IMU) provides monitoring of head movements. The EEG amplifier operates with 24-bit resolution up to 500 Hz sampling frequency and can communicate with other devices using 802.11 b/g/n WiFi. It has high signal-to-noise ratio (SNR) and common-mode rejection ratio (CMRR) (121 dB and 110 dB, respectively) and low input noise. In closed-loop BCI mode, the system can operate at 40 Hz, including real-time adaptive noise cancellation and 512 MB of processor memory. It supports LabVIEW as a backend coding language and JavaScript (JS), Cascading Style Sheets (CSS), and HyperText Markup Language (HTML) as front-end coding languages and includes training and optimization of support vector machine (SVM) neural classifiers. Extensive bench testing supports the technical specifications and human-subject pilot testing of a closed-loop BCI application to support upper-limb rehabilitation and provides proof-of-concept validation for the device's use at both the clinic and at home. Significance: The usability, interoperability, portability, reliability, and programmability of the proposed wireless closed-loop BCI system provides a low-cost solution for BCI and neurorehabilitation research and IoT applications.

Lateralized Neural Entropy modulates with Grip Force during Precision Grasping.

When holding a coffee mug filled to the brim, we strive to avoid spilling the coffee. This ability relies on the neural processes underlying the control of finger forces on a moment-to-moment basis. The brain activity lateralized to the contralateral hemisphere averaged over a trial and across the trials is known to be associated with the magnitude of grip force applied on an object. However, the mechanistic involvement of the variability in neural signals during grip force control remains unclear. In this study, we examined the dependence of neural variability over the frontal, central, and parietal regions assessed using noninvasive electroencephalography (EEG) on grip force magnitude during an isometric force control task. We hypothesized laterally specific modulation in EEG variability with higher magnitude of the grip force exerted during grip force control. We utilized an existing EEG dataset (64 channel) comprised of healthy young adults, who performed an isometric force control task while receiving visual feedback of the force applied. The force magnitude to be exerted on the instrumented object was cued to participants during the task, and varied pseudorandomly among 5, 10, and 15% of their maximum voluntary contraction (MVC) across the trials. We quantified neural variability via sample entropy (sequence-dependent measure) and standard deviation (sequence-independent measure) of the temporal EEG signal over the frontal, central, and parietal electrodes. The EEG sample entropy over the central electrodes showed lateralized, nonlinear, localized, modulation with force magnitude. Similar modulation was not observed over frontal or parietal EEG activity, nor for standard deviation in the EEG activity. Our findings highlight specificity in neural control of grip forces by demonstrating the modulation in sequence-dependent but not sequence-independent component of EEG variability. This modulation appeared to be lateralized, spatially constrained, and functionally dependent on the grip force magnitude. We discuss the relevance of these findings in scenarios where a finer precision is essential to enable grasp application, such as prosthesis and associated neural signal integration, and propose directions for future studies investigating the mechanistic role of neural entropy in grip force control.

Distinct Kinematic and Neuromuscular Activation Strategies During Quiet Stance and in Response to Postural Perturbations in Healthy Individuals Fitted With and Without a Lower-Limb Exoskeleton.

Many individuals with disabling conditions have difficulty with gait and balance control that may result in a fall. Exoskeletons are becoming an increasingly popular technology to aid in walking. Despite being a significant aid in increasing mobility, little attention has been paid to exoskeleton features to mitigate falls. To develop improved exoskeleton stability, quantitative information regarding how a user reacts to postural challenges while wearing the exoskeleton is needed. Assessing the unique responses of individuals to postural perturbations while wearing an exoskeleton provides critical information necessary to effectively accommodate a variety of individual response patterns. This report provides kinematic and neuromuscular data obtained from seven healthy, college-aged individuals during posterior support surface translations with and without wearing a lower limb exoskeleton. A 2-min, static baseline standing trial was also obtained. Outcome measures included a variety of 0 dimensional (OD) measures such as center of pressure (COP) RMS, peak amplitude, velocities, pathlength, and electromyographic (EMG) RMS, and peak amplitudes. These measures were obtained during epochs associated with the response to the perturbations: baseline, response, and recovery. T-tests were used to explore potential statistical differences between the exoskeleton and no exoskeleton conditions. Time series waveforms (1D) of the COP and EMG data were also analyzed. Statistical parametric mapping (SPM) was used to evaluate the 1D COP and EMG waveforms obtained during the epochs with and without wearing the exoskeleton. The results indicated that during quiet stance, COP velocity was increased while wearing the exoskeleton, but the magnitude of sway was unchanged. The OD COP measures revealed that wearing the exoskeleton significantly reduced the sway magnitude and velocity in response to the perturbations. There were no systematic effects of wearing the exoskeleton on EMG. SPM analysis revealed that there was a range of individual responses; both behaviorally (COP) and among neuromuscular activation patterns (EMG). Using both the OD and 1D measures provided a more comprehensive representation of how wearing the exoskeleton impacts the responses to posterior perturbations. This study supports a growing body of evidence that exoskeletons must be personalized to meet the specific capabilities and needs of each individual end-user.

Effects of transcutaneous spinal stimulation on spatiotemporal cortical activation patterns: a proof-of-concept EEG study.

Objective.Transcutaneous spinal cord stimulation (TSS) has been shown to be a promising non-invasive alternative to epidural spinal cord stimulation for improving outcomes of people with spinal cord injury (SCI). However, studies on the effects of TSS on cortical activation are limited. Our objectives were to evaluate the spatiotemporal effects of TSS on brain activity, and determine changes in functional connectivity under several different stimulation conditions. As a control, we also assessed the effects of functional electrical stimulation (FES) on cortical activity.Approach. Non-invasive scalp electroencephalography (EEG) was recorded during TSS or FES while five neurologically intact participants performed one of three lower-limb tasks while in the supine position: (1) A no contraction control task, (2) a rhythmic contraction task, or (3) a tonic contraction task. After EEG denoising and segmentation, independent components (ICs) were clustered across subjects to characterize sensorimotor networks in the time and frequency domains. ICs of the event related potentials (ERPs) were calculated for each cluster and condition. Next, a Generalized Partial Directed Coherence (gPDC) analysis was performed on each cluster to compare the functional connectivity between conditions and tasks.Main results. IC analysis of EEG during TSS resulted in three clusters identified at Brodmann areas (BA) 9, BA 6, and BA 4, which are areas associated with working memory, planning, and movement control. Lastly, we found significant (p < 0.05, adjusted for multiple comparisons) increases and decreases in functional connectivity of clusters during TSS, but not during FES when compared to the no stimulation conditions.Significance.The findings from this study provide evidence of how TSS recruits cortical networks during tonic and rhythmic lower limb movements. These results have implications for the development of spinal cord-based computer interfaces, and the design of neural stimulation devices for the treatment of pain and sensorimotor deficit.

Decoding neural activity preceding balance loss during standing with a lower-limb exoskeleton using an interpretable deep learning model.

Objective:Falls are a leading cause of death in adults 65 and older. Recent efforts to restore lower-limb function in these populations have seen an increase in the use of wearable robotic systems; however, fall prevention measures in these systems require early detection of balance loss to be effective. Prior studies have investigated whether kinematic variables contain information about an impending fall, but few have examined the potential of using electroencephalography (EEG) as a fall-predicting signal and how the brain responds to avoid a fall.Approach:To address this, we decoded neural activity in a balance perturbation task while wearing an exoskeleton. We acquired EEG, electromyography (EMG), and center of pressure (COP) data from seven healthy participants during mechanical perturbations while standing. The timing of the perturbations was randomized in all trials.Main results:We found perturbation evoked potentials (PEP) components as early as 75-134 ms after the onset of the external perturbation, which preceded both the peak in EMG (∼180 ms) and the COP (∼350 ms). A convolutional neural network trained to predict balance perturbations from single-trial EEG had a mean F-score of 75.0 ± 4.3%. Clustering GradCAM-based model explanations demonstrated that the model utilized components in the PEP and was not driven by artifacts. Additionally, dynamic functional connectivity results agreed with model explanations; the nodal connectivity measured using phase difference derivative was higher in the occipital-parietal region in the early stage of perturbations, before shifting to the parietal, motor, and back to the frontal-parietal channels. Continuous-time decoding of COP trajectories from EEG, using a gated recurrent unit model, achieved a mean Pearson's correlation coefficient of 0.7 ± 0.06.Significance:Overall, our findings suggest that EEG signals contain short-latency neural information related to an impending fall, which may be useful for developing brain-machine interface systems for fall prevention in robotic exoskeletons.

Assessment of Biomechanical Predictors of Occurrence of Low-Amplitude N1 Potentials Evoked by Naturally Occurring Postural Instabilities.

Naturally occurring postural instabilities that occur while standing and walking elicit specific cortical responses in the fronto-central regions (N1 potentials) followed by corrective balance responses to prevent falling. However, no framework could simultaneously track different biomechanical parameters preceding N1s, predict N1s, and assess their predictive power. Here, we propose a framework and show its utility by examining cortical activity (through electroencephalography [EEG]), ground reaction forces, and head acceleration in the anterior-posterior (AP) direction. Ten healthy young adults carried out a balance task of standing on a support surface with or without sway referencing in the AP direction, amplifying, or dampening natural body sway. Using independent components from the fronto-central cortical region obtained from subject-specific head models, we first robustly validated a prior approach on identifying low-amplitude N1 potentials before early signs of balance corrections. Then, a machine learning algorithm was used to evaluate different biomechanical parameters obtained before N1 potentials, to predict the occurrence of N1s. When different biomechanical parameters were directly compared, the time to boundary (TTB) was found to be the best predictor of the occurrence of upcoming low-amplitude N1 potentials during a balance task. Based on these findings, we confirm that the spatio-temporal characteristics of the center of pressure (COP) might serve as an essential parameter that can facilitate the early detection of postural instability in a balance task. Extending our framework to identify such biomarkers in dynamic situations like walking might improve the implementation of corrective balance responses through brain-machine-interfaces to reduce falls in the elderly.

Brain-to-brain communication during musical improvisation: a performance case study.

Understanding and predicting others' actions in ecological settings is an important research goal in social neuroscience. Here, we deployed a mobile brain-body imaging (MoBI) methodology to analyze inter-brain communication between professional musicians during a live jazz performance. Specifically, bispectral analysis was conducted to assess the synchronization of scalp electroencephalographic (EEG) signals from three expert musicians during a three-part 45 minute jazz performance, during which a new musician joined every five minutes. The bispectrum was estimated for all musician dyads, electrode combinations, and five frequency bands. The results showed higher bispectrum in the beta and gamma frequency bands (13-50 Hz) when more musicians performed together, and when they played a musical phrase synchronously. Positive bispectrum amplitude changes were found approximately three seconds prior to the identified synchronized performance events suggesting preparatory cortical activity predictive of concerted behavioral action. Moreover, a higher amount of synchronized EEG activity, across electrode regions, was observed as more musicians performed, with inter-brain synchronization between the temporal, parietal, and occipital regions the most frequent. Increased synchrony between the musicians' brain activity reflects shared multi-sensory processing and movement intention in a musical improvisation task.

A Mixed Filtering Approach for Real-Time Seizure State Tracking Using Multi-Channel Electroencephalography Data.

Real-time continuous tracking of seizure state is necessary to develop feedback neuromodulation therapy that can prevent or terminate a seizure early. Due to its high temporal resolution, high scalp coverage, and non-invasive applicability, electroencephalography (EEG) is a good candidate for seizure tracking. In this research, we make multiple seizure state estimations using a mixed-filter and multiple channels found over the entire sensor space; then by applying a Kalman filter, we produce a single seizure state estimation made up of these individual estimations. Using a modified wrapper feature selection, we determine two optimal features of mixed data type, one continuous and one binary analyzing all available channels. These features are used in a state-space framework to model the continuous hidden seizure state. Expectation maximization is performed offline on the training and validation data sets to estimate unknown parameters. The seizure state estimation process is performed for multiple channels, and the seizure state estimation is derived using a square-root Kalman filter. A second expectation maximization step is utilized to estimate the unknown square-root Kalman filter parameters. This method is tested in a real-time applicable way for seizure state estimation. Applying this approach, we obtain a single seizure state estimation with quantitative information about the likelihood of a seizure occurring, which we call seizure probability. Our results on the experimental data (CHB-MIT EEG database) validate the proposed estimation method and we achieve an average accuracy, sensitivity, and specificity of 92.7%, 92.8%, and 93.4%, respectively. The potential applications of this seizure estimation model are for closed-loop neuromodulation and long-term quantitative analysis of seizure treatment efficacy.

Effects of an exoskeleton-assisted gait training on post-stroke lower-limb muscle coordination.

Objective.Powered exoskeletons have been used to help persons with gait impairment regain some walking ability. However, little is known about its impact on neuromuscular coordination in persons with stroke. The objective of this study is to investigate how a powered exoskeleton could affect the neuromuscular coordination of persons with post-stroke hemiparesis.Approach.Eleven able-bodied subjects and ten stroke subjects participated in a single-visit treadmill walking assessment, in which their motion and lower-limb muscle activities were captured. By comparing spatiotemporal parameters, kinematics, and muscle synergy pattern between two groups, we characterized the normal gait pattern and the post-stroke motor deficits. Five eligible stroke subjects received exoskeleton-assisted gait trainings and walking assessments were conducted pre-intervention (Pre) and post-intervention (Post), without (WO) and with (WT) the exoskeleton. We compared their gait performance between (a) Pre and Post to investigate the effect of exoskeleton-assisted gait training and, (b) WO and WT the exoskeleton to investigate the effect of exoskeleton wearing on stroke subjects.Main results.While four distinct motor modules were needed to describe lower-extremity activities during stead-speed walking among able-bodied subjects, three modules were sufficient for the paretic leg from the stroke subjects. Muscle coordination complexity, module composition and activation timing were preserved after the training, indicating the intervention did not significantly change the neuromuscular coordination. In contrast, walking WT the exoskeleton altered the stroke subjects' synergy pattern, especially on the paretic side. The changes were dominated by the activation profile modulation towards the normal pattern observed from the able-bodied group.Significance.This study gave us some critical insight into how a powered exoskeleton affects the stroke subjects' neuromuscular coordination during gait and demonstrated the potential to use muscle synergy as a method to evaluate the effect of the exoskeleton training.This study was registered at ClinicalTrials.gov (identifier: NCT03057652).

Meeting Proceedings for SCI 2020: Launching a Decade of Disruption in Spinal Cord Injury Research.

The spinal cord injury (SCI) research community has experienced great advances in discovery research, technology development, and promising clinical interventions in the past decade. To build upon these advances and maximize the benefit to persons with SCI, the National Institutes of Health (NIH) hosted a conference February 12-13, 2019 titled "SCI 2020: Launching a Decade of Disruption in Spinal Cord Injury Research." The purpose of the conference was to bring together a broad range of stakeholders, including researchers, clinicians and healthcare professionals, persons with SCI, industry partners, regulators, and funding agency representatives to break down existing communication silos. Invited speakers were asked to summarize the state of the science, assess areas of technological and community readiness, and build collaborations that could change the trajectory of research and clinical options for people with SCI. In this report, we summarize the state of the science in each of five key domains and identify the gaps in the scientific literature that need to be addressed to move the field forward.

Concerns in the Blurred Divisions between Medical and Consumer Neurotechnology.

Neurotechnology has traditionally been central to the diagnosis and treatment of neurological disorders. While these devices have initially been utilized in clinical and research settings, recent advancements in neurotechnology have yielded devices that are more portable, user-friendly, and less expensive. These improvements allow laypeople to monitor their brain waves and interface their brains with external devices. Such improvements have led to the rise of wearable neurotechnology that is marketed to the consumer. While many of the consumer devices are marketed for innocuous applications, such as use in video games, there is potential for them to be repurposed for medical use. How do we manage neurotechnologies that skirt the line between medical and consumer applications and what can be done to ensure consumer safety? Here, we characterize neurotechnology based on medical and consumer applications and summarize currently marketed uses of consumer-grade wearable headsets. We lay out concerns that may arise due to the similar claims associated with both medical and consumer devices, the possibility of consumer devices being repurposed for medical uses, and the potential for medical uses of neurotechnology to influence commercial markets related to employment and self-enhancement.

A Roadmap Towards Standards for Neurally Controlled End Effectors.

The control and manipulation of various types of end effectors such as powered exoskeletons, prostheses, and 'neural' cursors by brain-machine interface (BMI) systems has been the target of many research projects. A seamless "plug and play" interface between any BMI and end effector is desired, wherein similar user's intent cause similar end effectors to behave identically. This report is based on the outcomes of an IEEE Standards Association Industry Connections working group on End Effectors for Brain-Machine Interfacing that convened to identify and address gaps in the existing standards for BMI-based solutions with a focus on the end-effector component. A roadmap towards standardization of end effectors for BMI systems is discussed by identifying current device standards that are applicable for end effectors. While current standards address basic electrical and mechanical safety, and to some extent, performance requirements, several gaps exist pertaining to unified terminologies, data communication protocols, patient safety and risk mitigation.

Sensory Integration in Human Movement: A New Brain-Machine Interface Based on Gamma Band and Attention Level for Controlling a Lower-Limb Exoskeleton.

Brain-machine interfaces (BMIs) can improve the control of assistance mobility devices making its use more intuitive and natural. In the case of an exoskeleton, they can also help rehabilitation therapies due to the reinforcement of neuro-plasticity through repetitive motor actions and cognitive engagement of the subject. Therefore, the cognitive implication of the user is a key aspect in BMI applications, and it is important to assure that the mental task correlates with the actual motor action. However, the process of walking is usually an autonomous mental task that requires a minimal conscious effort. Consequently, a brain-machine interface focused on the attention to gait could facilitate sensory integration in individuals with neurological impairment through the analysis of voluntary gait will and its repetitive use. This way the combined use of BMI+exoskeleton turns from assistance to restoration. This paper presents a new brain-machine interface based on the decoding of gamma band activity and attention level during motor imagery mental tasks. This work also shows a case study tested in able-bodied subjects prior to a future clinical study, demonstrating that a BMI based on gamma band and attention-level paradigm allows real-time closed-loop control of a Rex exoskeleton.

Towards a Portable Magnetoencephalography Based Brain Computer Interface with Optically-Pumped Magnetometers.

Brain Computer Interfaces (BCIs) allow individuals to control devices, machines and prostheses with their thoughts. Most feasibility studies with BCIs have utilized scalp electroencephalography (EEG), due to it being accessible, noninvasive, and portable. While BCIs have been studied with magnetoencephalography (MEG), the modality has limited applications due to the large immobile hardware. Here we propose that room-temperature, optically-pumped magnetometers (OPMs) can potentially serve a portable modality that can be used for BCIs. OPMs have the added advantage that low-frequency neuromagnetic fields are not affected by volume conduction, which is known to distort EEG signals. In this feasibility study, we tested an OPM system with a real-time BCI where able bodied participants controlled a cursor to reach two targets. This BCI system used alpha and beta-band power modulations associated with hand movements. Our preliminary results show significant alpha and beta-band desynchronization due to movement, as found in previous literature.